class_docstr: Simple feedforward neural network that subtracts trend before forecasting [Zeng2023]_.
  
  Based on `gluonts.torch.model.d_linear.DLinearEstimator <https://ts.gluon.ai/stable/api/gluonts/gluonts.torch.model.d_linear.html>`_.
  See GluonTS documentation for additional hyperparameters.
  
  References
  ----------
  .. [Zeng2023] Zeng, Ailing, et al.
      "Are transformers effective for time series forecasting?"
      AAAI Conference on Artificial Intelligence. 2023.
  
  Other Parameters
  ----------------
  context_length : int, default = 96
      Number of time units that condition the predictions
  hidden_dimension: int, default = 20
      Size of hidden layers in the feedforward network
  distr_output : gluonts.torch.distributions.DistributionOutput, default = StudentTOutput()
      Distribution to fit.
  scaling : {"mean", "std", None}, default = "mean"
      Scaling applied to the inputs. One of ``"mean"`` (mean absolute scaling), ``"std"`` (standardization), ``None`` (no scaling).
  max_epochs : int, default = 100
      Number of epochs the model will be trained for
  batch_size : int, default = 64
      Size of batches used during training
  predict_batch_size : int, default = 500
      Size of batches used during prediction.
  num_batches_per_epoch : int, default = 50
      Number of batches processed every epoch
  lr : float, default = 1e-3,
      Learning rate used during training
  trainer_kwargs : dict, optional
      Optional keyword arguments passed to ``lightning.Trainer``.
  early_stopping_patience : int or None, default = 20
      Early stop training if the validation loss doesn't improve for this many epochs.
  weight_decay : float, default = 1e-8
      Weight decay regularization parameter.
code: def default_context_length(self) -> int:
      return 96
  
imports: import logging
  from typing import Any, Dict, Type
  from gluonts.model.estimator import Estimator
  from autogluon.timeseries.models.gluonts.abstract_gluonts import AbstractGluonTSModel
  from autogluon.timeseries.utils.datetime import get_lags_for_frequency, get_seasonality, get_time_features_for_frequency
label_desc: Use image node_id 1 for calling the DLinearModel obj's underlying member method code with example usage: obj.default_context_length() and returns: int